Overview

Dataset statistics

Number of variables15
Number of observations280
Missing cells197
Missing cells (%)4.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory32.9 KiB
Average record size in memory120.5 B

Variable types

Numeric13
Categorical2

Alerts

Province/State has a high cardinality: 87 distinct values High cardinality
Country/Region has a high cardinality: 196 distinct values High cardinality
1/24/2022 is highly correlated with 1/25/2022 and 8 other fieldsHigh correlation
1/25/2022 is highly correlated with 1/24/2022 and 8 other fieldsHigh correlation
1/26/2022 is highly correlated with 1/24/2022 and 8 other fieldsHigh correlation
1/27/2022 is highly correlated with 1/24/2022 and 8 other fieldsHigh correlation
1/28/2022 is highly correlated with 1/24/2022 and 8 other fieldsHigh correlation
1/29/2022 is highly correlated with 1/24/2022 and 8 other fieldsHigh correlation
1/30/2022 is highly correlated with 1/24/2022 and 8 other fieldsHigh correlation
1/31/2022 is highly correlated with 1/24/2022 and 8 other fieldsHigh correlation
2/1/2022 is highly correlated with 1/24/2022 and 8 other fieldsHigh correlation
2/2/2022 is highly correlated with 1/24/2022 and 8 other fieldsHigh correlation
1/24/2022 is highly correlated with 1/25/2022 and 8 other fieldsHigh correlation
1/25/2022 is highly correlated with 1/24/2022 and 8 other fieldsHigh correlation
1/26/2022 is highly correlated with 1/24/2022 and 8 other fieldsHigh correlation
1/27/2022 is highly correlated with 1/24/2022 and 8 other fieldsHigh correlation
1/28/2022 is highly correlated with 1/24/2022 and 8 other fieldsHigh correlation
1/29/2022 is highly correlated with 1/24/2022 and 8 other fieldsHigh correlation
1/30/2022 is highly correlated with 1/24/2022 and 8 other fieldsHigh correlation
1/31/2022 is highly correlated with 1/24/2022 and 8 other fieldsHigh correlation
2/1/2022 is highly correlated with 1/24/2022 and 8 other fieldsHigh correlation
2/2/2022 is highly correlated with 1/24/2022 and 8 other fieldsHigh correlation
1/24/2022 is highly correlated with 1/25/2022 and 8 other fieldsHigh correlation
1/25/2022 is highly correlated with 1/24/2022 and 8 other fieldsHigh correlation
1/26/2022 is highly correlated with 1/24/2022 and 8 other fieldsHigh correlation
1/27/2022 is highly correlated with 1/24/2022 and 8 other fieldsHigh correlation
1/28/2022 is highly correlated with 1/24/2022 and 8 other fieldsHigh correlation
1/29/2022 is highly correlated with 1/24/2022 and 8 other fieldsHigh correlation
1/30/2022 is highly correlated with 1/24/2022 and 8 other fieldsHigh correlation
1/31/2022 is highly correlated with 1/24/2022 and 8 other fieldsHigh correlation
2/1/2022 is highly correlated with 1/24/2022 and 8 other fieldsHigh correlation
2/2/2022 is highly correlated with 1/24/2022 and 8 other fieldsHigh correlation
Unnamed: 0 is highly correlated with Province/State and 1 other fieldsHigh correlation
Province/State is highly correlated with Unnamed: 0 and 2 other fieldsHigh correlation
Lat is highly correlated with Province/State and 1 other fieldsHigh correlation
Long is highly correlated with Unnamed: 0 and 2 other fieldsHigh correlation
1/24/2022 is highly correlated with 1/25/2022 and 8 other fieldsHigh correlation
1/25/2022 is highly correlated with 1/24/2022 and 8 other fieldsHigh correlation
1/26/2022 is highly correlated with 1/24/2022 and 8 other fieldsHigh correlation
1/27/2022 is highly correlated with 1/24/2022 and 8 other fieldsHigh correlation
1/28/2022 is highly correlated with 1/24/2022 and 8 other fieldsHigh correlation
1/29/2022 is highly correlated with 1/24/2022 and 8 other fieldsHigh correlation
1/30/2022 is highly correlated with 1/24/2022 and 8 other fieldsHigh correlation
1/31/2022 is highly correlated with 1/24/2022 and 8 other fieldsHigh correlation
2/1/2022 is highly correlated with 1/24/2022 and 8 other fieldsHigh correlation
2/2/2022 is highly correlated with 1/24/2022 and 8 other fieldsHigh correlation
Province/State has 193 (68.9%) missing values Missing
Unnamed: 0 is uniformly distributed Uniform
Province/State is uniformly distributed Uniform
Unnamed: 0 has unique values Unique
Lat has 4 (1.4%) zeros Zeros
Long has 4 (1.4%) zeros Zeros

Reproduction

Analysis started2022-02-04 04:05:30.397364
Analysis finished2022-02-04 04:05:54.567660
Duration24.17 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ≥0)

HIGH CORRELATION
UNIFORM
UNIQUE

Distinct280
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean139.5
Minimum0
Maximum279
Zeros1
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2022-02-04T15:05:54.661706image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile13.95
Q169.75
median139.5
Q3209.25
95-th percentile265.05
Maximum279
Range279
Interquartile range (IQR)139.5

Descriptive statistics

Standard deviation80.97324661
Coefficient of variation (CV)0.5804533807
Kurtosis-1.2
Mean139.5
Median Absolute Deviation (MAD)70
Skewness0
Sum39060
Variance6556.666667
MonotonicityStrictly increasing
2022-02-04T15:05:54.785292image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
0.4%
1841
 
0.4%
1901
 
0.4%
1891
 
0.4%
1881
 
0.4%
1871
 
0.4%
1861
 
0.4%
1851
 
0.4%
1831
 
0.4%
1921
 
0.4%
Other values (270)270
96.4%
ValueCountFrequency (%)
01
0.4%
11
0.4%
21
0.4%
31
0.4%
41
0.4%
51
0.4%
61
0.4%
71
0.4%
81
0.4%
91
0.4%
ValueCountFrequency (%)
2791
0.4%
2781
0.4%
2771
0.4%
2761
0.4%
2751
0.4%
2741
0.4%
2731
0.4%
2721
0.4%
2711
0.4%
2701
0.4%

Province/State
Categorical

HIGH CARDINALITY
HIGH CORRELATION
MISSING
UNIFORM

Distinct87
Distinct (%)100.0%
Missing193
Missing (%)68.9%
Memory size2.3 KiB
Australian Capital Territory
 
1
Martinique
 
1
Guadeloupe
 
1
French Polynesia
 
1
French Guiana
 
1
Other values (82)
82 

Length

Max length44
Median length9
Mean length11.40229885
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique87 ?
Unique (%)100.0%

Sample

1st rowAustralian Capital Territory
2nd rowNew South Wales
3rd rowNorthern Territory
4th rowQueensland
5th rowSouth Australia

Common Values

ValueCountFrequency (%)
Australian Capital Territory1
 
0.4%
Martinique1
 
0.4%
Guadeloupe1
 
0.4%
French Polynesia1
 
0.4%
French Guiana1
 
0.4%
Greenland1
 
0.4%
Faroe Islands1
 
0.4%
Zhejiang1
 
0.4%
Yunnan1
 
0.4%
Xinjiang1
 
0.4%
Other values (77)77
 
27.5%
(Missing)193
68.9%

Length

2022-02-04T15:05:54.896286image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
islands7
 
5.0%
and6
 
4.3%
saint3
 
2.1%
new3
 
2.1%
south2
 
1.4%
sint2
 
1.4%
australia2
 
1.4%
british2
 
1.4%
french2
 
1.4%
princess2
 
1.4%
Other values (109)110
78.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Country/Region
Categorical

HIGH CARDINALITY

Distinct196
Distinct (%)70.0%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
China
34 
Canada
 
16
United Kingdom
 
12
France
 
12
Australia
 
8
Other values (191)
198 

Length

Max length32
Median length7
Mean length8.139285714
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique188 ?
Unique (%)67.1%

Sample

1st rowAfghanistan
2nd rowAlbania
3rd rowAlgeria
4th rowAndorra
5th rowAngola

Common Values

ValueCountFrequency (%)
China34
 
12.1%
Canada16
 
5.7%
United Kingdom12
 
4.3%
France12
 
4.3%
Australia8
 
2.9%
Netherlands5
 
1.8%
Denmark3
 
1.1%
New Zealand2
 
0.7%
Panama1
 
0.4%
Niger1
 
0.4%
Other values (186)186
66.4%

Length

2022-02-04T15:05:55.006153image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
china34
 
9.9%
canada16
 
4.7%
united13
 
3.8%
kingdom12
 
3.5%
france12
 
3.5%
australia8
 
2.3%
and7
 
2.0%
netherlands5
 
1.5%
denmark3
 
0.9%
new3
 
0.9%
Other values (220)231
67.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Lat
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct274
Distinct (%)98.6%
Missing2
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean20.15604155
Minimum-51.7963
Maximum71.7069
Zeros4
Zeros (%)1.4%
Negative55
Negative (%)19.6%
Memory size2.3 KiB
2022-02-04T15:05:55.101834image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-51.7963
5-th percentile-26.664595
Q14.643279
median21.51717
Q340.39335025
95-th percentile55.200705
Maximum71.7069
Range123.5032
Interquartile range (IQR)35.75007125

Descriptive statistics

Standard deviation25.2833179
Coefficient of variation (CV)1.254379132
Kurtosis-0.3587458794
Mean20.15604155
Median Absolute Deviation (MAD)17.95728
Skewness-0.4359189955
Sum5603.37955
Variance639.2461638
MonotonicityNot monotonic
2022-02-04T15:05:55.333400image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04
 
1.4%
52.93992
 
0.7%
33.939111
 
0.4%
42.7086781
 
0.4%
28.16671
 
0.4%
-22.95761
 
0.4%
-18.6656951
 
0.4%
31.79171
 
0.4%
46.86251
 
0.4%
12.17841
 
0.4%
Other values (264)264
94.3%
(Missing)2
 
0.7%
ValueCountFrequency (%)
-51.79631
0.4%
-42.88211
0.4%
-40.90061
0.4%
-38.41611
0.4%
-37.81361
0.4%
-35.67511
0.4%
-35.47351
0.4%
-34.92851
0.4%
-33.86881
0.4%
-32.52281
0.4%
ValueCountFrequency (%)
71.70691
0.4%
70.29981
0.4%
64.96311
0.4%
64.82551
0.4%
64.28231
0.4%
61.924111
0.4%
61.89261
0.4%
61.524011
0.4%
60.4721
0.4%
60.1281611
0.4%

Long
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct275
Distinct (%)98.9%
Missing2
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean21.78895473
Minimum-178.1165
Maximum178.065
Zeros4
Zeros (%)1.4%
Negative93
Negative (%)33.2%
Memory size2.3 KiB
2022-02-04T15:05:55.437300image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-178.1165
5-th percentile-89.096645
Q1-37.713675
median20.9211885
Q384.992575
95-th percentile140.4009775
Maximum178.065
Range356.1815
Interquartile range (IQR)122.70625

Descriptive statistics

Standard deviation76.20016892
Coefficient of variation (CV)3.497192494
Kurtosis-0.4988627293
Mean21.78895473
Median Absolute Deviation (MAD)63.9222
Skewness-0.1476040244
Sum6057.329414
Variance5806.465744
MonotonicityNot monotonic
2022-02-04T15:05:55.566867image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04
 
1.4%
7.41671
 
0.4%
84.251
 
0.4%
18.49041
 
0.4%
35.5295621
 
0.4%
-7.09261
 
0.4%
19.374391
 
0.4%
103.84671
 
0.4%
67.7099531
 
0.4%
-69.96831
 
0.4%
Other values (265)265
94.6%
(Missing)2
 
0.7%
ValueCountFrequency (%)
-178.11651
0.4%
-175.19821
0.4%
-172.10461
0.4%
-168.7341
0.4%
-159.77771
0.4%
-1351
0.4%
-127.64761
0.4%
-124.84571
0.4%
-116.57651
0.4%
-106.45091
0.4%
ValueCountFrequency (%)
178.0651
0.4%
174.8861
0.4%
171.18451
0.4%
166.95921
0.4%
165.6180421
0.4%
160.15621
0.4%
153.02511
0.4%
151.20931
0.4%
150.55081
0.4%
149.40681
0.4%

1/24/2022
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct275
Distinct (%)98.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1268535.596
Minimum0
Maximum71783483
Zeros2
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2022-02-04T15:05:55.684545image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile29.95
Q17280.5
median63984.5
Q3605076.5
95-th percentile4554237.6
Maximum71783483
Range71783483
Interquartile range (IQR)597796

Descriptive statistics

Standard deviation5447710.697
Coefficient of variation (CV)4.294487842
Kurtosis110.6333879
Mean1268535.596
Median Absolute Deviation (MAD)63756
Skewness9.607524292
Sum355189967
Variance2.967755183 × 1013
MonotonicityNot monotonic
2022-02-04T15:05:55.797392image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13
 
1.1%
72
 
0.7%
02
 
0.7%
132
 
0.7%
1598961
 
0.4%
76911
 
0.4%
2226521
 
0.4%
11011631
 
0.4%
2138961
 
0.4%
4303281
 
0.4%
Other values (265)265
94.6%
ValueCountFrequency (%)
02
0.7%
13
1.1%
21
 
0.4%
41
 
0.4%
72
0.7%
91
 
0.4%
132
0.7%
201
 
0.4%
291
 
0.4%
301
 
0.4%
ValueCountFrequency (%)
717834831
0.4%
397992021
0.4%
241420321
0.4%
164468521
0.4%
159536851
0.4%
110141521
0.4%
109880271
0.4%
100013441
0.4%
92808901
0.4%
89095031
0.4%

1/25/2022
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct275
Distinct (%)98.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1281805.504
Minimum0
Maximum72333439
Zeros2
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2022-02-04T15:05:55.920910image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile29.95
Q17423.75
median64181
Q3611787
95-th percentile4591675.45
Maximum72333439
Range72333439
Interquartile range (IQR)604363.25

Descriptive statistics

Standard deviation5494230.917
Coefficient of variation (CV)4.286321834
Kurtosis110.2214282
Mean1281805.504
Median Absolute Deviation (MAD)63952.5
Skewness9.585828067
Sum358905541
Variance3.018657337 × 1013
MonotonicityNot monotonic
2022-02-04T15:05:56.055296image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13
 
1.1%
72
 
0.7%
02
 
0.7%
132
 
0.7%
1602521
 
0.4%
77901
 
0.4%
2228921
 
0.4%
11075251
 
0.4%
2150171
 
0.4%
4334081
 
0.4%
Other values (265)265
94.6%
ValueCountFrequency (%)
02
0.7%
13
1.1%
21
 
0.4%
41
 
0.4%
72
0.7%
91
 
0.4%
132
0.7%
261
 
0.4%
291
 
0.4%
301
 
0.4%
ValueCountFrequency (%)
723334391
0.4%
400851161
0.4%
243423221
0.4%
169484871
0.4%
160477161
0.4%
110904931
0.4%
110552461
0.4%
102126211
0.4%
93957671
0.4%
90886721
0.4%

1/26/2022
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct275
Distinct (%)98.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1295233.786
Minimum0
Maximum72980648
Zeros2
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2022-02-04T15:05:56.167919image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile29.95
Q17583
median64455.5
Q3623113.25
95-th percentile4642420.65
Maximum72980648
Range72980648
Interquartile range (IQR)615530.25

Descriptive statistics

Standard deviation5544259.445
Coefficient of variation (CV)4.280508667
Kurtosis110.0823882
Mean1295233.786
Median Absolute Deviation (MAD)64144
Skewness9.57685628
Sum362665460
Variance3.07388128 × 1013
MonotonicityNot monotonic
2022-02-04T15:05:56.307497image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13
 
1.1%
72
 
0.7%
02
 
0.7%
132
 
0.7%
1606921
 
0.4%
77901
 
0.4%
2231121
 
0.4%
11145271
 
0.4%
2159501
 
0.4%
4356811
 
0.4%
Other values (265)265
94.6%
ValueCountFrequency (%)
02
0.7%
13
1.1%
21
 
0.4%
41
 
0.4%
72
0.7%
91
 
0.4%
132
0.7%
281
 
0.4%
291
 
0.4%
301
 
0.4%
ValueCountFrequency (%)
729806481
0.4%
403715001
0.4%
245600931
0.4%
173105481
0.4%
161493191
0.4%
111679271
0.4%
111293181
0.4%
103835611
0.4%
95293201
0.4%
93172801
0.4%

1/27/2022
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct273
Distinct (%)97.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1308330.621
Minimum0
Maximum73465396
Zeros2
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2022-02-04T15:05:56.491295image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile29.95
Q17588
median65063
Q3633146
95-th percentile4699628.05
Maximum73465396
Range73465396
Interquartile range (IQR)625558

Descriptive statistics

Standard deviation5586263.727
Coefficient of variation (CV)4.269764565
Kurtosis109.6495402
Mean1308330.621
Median Absolute Deviation (MAD)64751.5
Skewness9.55413241
Sum366332574
Variance3.120634243 × 1013
MonotonicityNot monotonic
2022-02-04T15:05:56.598935image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13
 
1.1%
292
 
0.7%
72
 
0.7%
02
 
0.7%
1612
 
0.7%
132
 
0.7%
77901
 
0.4%
2234131
 
0.4%
11200871
 
0.4%
2168561
 
0.4%
Other values (263)263
93.9%
ValueCountFrequency (%)
02
0.7%
13
1.1%
21
 
0.4%
41
 
0.4%
72
0.7%
91
 
0.4%
132
0.7%
292
0.7%
301
 
0.4%
791
 
0.4%
ValueCountFrequency (%)
734653961
0.4%
406227091
0.4%
247897951
0.4%
176967961
0.4%
162454741
0.4%
112501071
0.4%
112174231
0.4%
105396011
0.4%
96602081
0.4%
94776031
0.4%

1/28/2022
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct274
Distinct (%)97.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1321258.539
Minimum0
Maximum74063641
Zeros2
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2022-02-04T15:05:56.737912image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile29.95
Q17595
median65826.5
Q3645241.5
95-th percentile4758743.05
Maximum74063641
Range74063641
Interquartile range (IQR)637646.5

Descriptive statistics

Standard deviation5633267.941
Coefficient of variation (CV)4.263562182
Kurtosis109.4519608
Mean1321258.539
Median Absolute Deviation (MAD)65514.5
Skewness9.541996065
Sum369952391
Variance3.173370769 × 1013
MonotonicityNot monotonic
2022-02-04T15:05:56.889553image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13
 
1.1%
292
 
0.7%
72
 
0.7%
02
 
0.7%
132
 
0.7%
81831
 
0.4%
1557221
 
0.4%
2236121
 
0.4%
11249861
 
0.4%
2177521
 
0.4%
Other values (264)264
94.3%
ValueCountFrequency (%)
02
0.7%
13
1.1%
21
 
0.4%
41
 
0.4%
72
0.7%
91
 
0.4%
132
0.7%
292
0.7%
301
 
0.4%
791
 
0.4%
ValueCountFrequency (%)
740636411
0.4%
408582411
0.4%
250506011
0.4%
180485841
0.4%
163339801
0.4%
113436931
0.4%
113147071
0.4%
106839481
0.4%
97791301
0.4%
96616571
0.4%

1/29/2022
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct274
Distinct (%)97.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1330534.014
Minimum0
Maximum74232238
Zeros2
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2022-02-04T15:05:57.016055image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile30
Q17605.25
median65921.5
Q3651969
95-th percentile4809977.65
Maximum74232238
Range74232238
Interquartile range (IQR)644363.75

Descriptive statistics

Standard deviation5657957.555
Coefficient of variation (CV)4.252396026
Kurtosis108.649979
Mean1330534.014
Median Absolute Deviation (MAD)65693.5
Skewness9.504590895
Sum372549524
Variance3.201248369 × 1013
MonotonicityNot monotonic
2022-02-04T15:05:57.152070image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13
 
1.1%
72
 
0.7%
02
 
0.7%
132
 
0.7%
302
 
0.7%
1612901
 
0.4%
4382491
 
0.4%
11291401
 
0.4%
2186371
 
0.4%
4421861
 
0.4%
Other values (264)264
94.3%
ValueCountFrequency (%)
02
0.7%
13
1.1%
21
 
0.4%
41
 
0.4%
72
0.7%
91
 
0.4%
132
0.7%
291
 
0.4%
302
0.7%
791
 
0.4%
ValueCountFrequency (%)
742322381
0.4%
410925221
0.4%
252561981
0.4%
183809821
0.4%
164061231
0.4%
114384761
0.4%
114270091
0.4%
108213751
0.4%
97791301
0.4%
97748471
0.4%

1/30/2022
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct274
Distinct (%)97.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1338493.611
Minimum0
Maximum74424305
Zeros2
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2022-02-04T15:05:57.468014image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile30
Q17613
median66017
Q3657573.25
95-th percentile4856546.6
Maximum74424305
Range74424305
Interquartile range (IQR)649960.25

Descriptive statistics

Standard deviation5680180.306
Coefficient of variation (CV)4.243711184
Kurtosis108.1432986
Mean1338493.611
Median Absolute Deviation (MAD)65788
Skewness9.48058053
Sum374778211
Variance3.226444831 × 1013
MonotonicityNot monotonic
2022-02-04T15:05:57.646652image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13
 
1.1%
72
 
0.7%
02
 
0.7%
132
 
0.7%
302
 
0.7%
1621111
 
0.4%
4382491
 
0.4%
11313951
 
0.4%
2193031
 
0.4%
4433921
 
0.4%
Other values (264)264
94.3%
ValueCountFrequency (%)
02
0.7%
13
1.1%
21
 
0.4%
41
 
0.4%
72
0.7%
91
 
0.4%
132
0.7%
291
 
0.4%
302
0.7%
791
 
0.4%
ValueCountFrequency (%)
744243051
0.4%
413024401
0.4%
253606471
0.4%
186304301
0.4%
164685221
0.4%
115473331
0.4%
115266211
0.4%
109254851
0.4%
98460321
0.4%
97791301
0.4%

1/31/2022
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct275
Distinct (%)98.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1351399.532
Minimum0
Maximum74951445
Zeros2
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2022-02-04T15:05:57.756268image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile31.9
Q17740
median67695
Q3660958.75
95-th percentile4888975.8
Maximum74951445
Range74951445
Interquartile range (IQR)653218.75

Descriptive statistics

Standard deviation5724079.153
Coefficient of variation (CV)4.235667555
Kurtosis107.7465791
Mean1351399.532
Median Absolute Deviation (MAD)67376.5
Skewness9.456784718
Sum378391869
Variance3.276508215 × 1013
MonotonicityNot monotonic
2022-02-04T15:05:57.908919image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13
 
1.1%
72
 
0.7%
02
 
0.7%
132
 
0.7%
1629261
 
0.4%
81831
 
0.4%
2238271
 
0.4%
11327161
 
0.4%
2197751
 
0.4%
4445881
 
0.4%
Other values (265)265
94.6%
ValueCountFrequency (%)
02
0.7%
13
1.1%
21
 
0.4%
41
 
0.4%
72
0.7%
91
 
0.4%
132
0.7%
291
 
0.4%
301
 
0.4%
321
 
0.4%
ValueCountFrequency (%)
749514451
0.4%
414694991
0.4%
254635301
0.4%
187130871
0.4%
173158931
0.4%
116703661
0.4%
116198821
0.4%
109831161
0.4%
100254631
0.4%
99612531
0.4%

2/1/2022
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct275
Distinct (%)98.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1363156.643
Minimum0
Maximum75350359
Zeros2
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2022-02-04T15:05:58.062475image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile31.9
Q17740
median68143
Q3667739.25
95-th percentile4928290.95
Maximum75350359
Range75350359
Interquartile range (IQR)659999.25

Descriptive statistics

Standard deviation5759551.991
Coefficient of variation (CV)4.225157851
Kurtosis107.3136391
Mean1363156.643
Median Absolute Deviation (MAD)67738
Skewness9.432953315
Sum381683860
Variance3.317243914 × 1013
MonotonicityNot monotonic
2022-02-04T15:05:58.178542image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13
 
1.1%
72
 
0.7%
02
 
0.7%
132
 
0.7%
1635551
 
0.4%
81831
 
0.4%
2239491
 
0.4%
11357961
 
0.4%
2205971
 
0.4%
4460761
 
0.4%
Other values (265)265
94.6%
ValueCountFrequency (%)
02
0.7%
13
1.1%
21
 
0.4%
41
 
0.4%
72
0.7%
91
 
0.4%
132
0.7%
291
 
0.4%
301
 
0.4%
321
 
0.4%
ValueCountFrequency (%)
753503591
0.4%
416308851
0.4%
256347811
0.4%
191127551
0.4%
174283451
0.4%
117950591
0.4%
117224831
0.4%
111164221
0.4%
102367401
0.4%
100391261
0.4%

2/2/2022
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct275
Distinct (%)98.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1374497.261
Minimum0
Maximum75680487
Zeros2
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2022-02-04T15:05:58.308271image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile32.85
Q17797.5
median68256.5
Q3675358.75
95-th percentile4981539.4
Maximum75680487
Range75680487
Interquartile range (IQR)667561.25

Descriptive statistics

Standard deviation5790886.629
Coefficient of variation (CV)4.213094339
Kurtosis106.8389889
Mean1374497.261
Median Absolute Deviation (MAD)67851.5
Skewness9.407782195
Sum384859233
Variance3.353436795 × 1013
MonotonicityNot monotonic
2022-02-04T15:05:58.477659image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
72
 
0.7%
12
 
0.7%
02
 
0.7%
132
 
0.7%
374262
 
0.7%
1641901
 
0.4%
4467701
 
0.4%
1560441
 
0.4%
2240431
 
0.4%
11388471
 
0.4%
Other values (265)265
94.6%
ValueCountFrequency (%)
02
0.7%
12
0.7%
21
0.4%
41
0.4%
51
0.4%
72
0.7%
91
0.4%
132
0.7%
291
0.4%
301
0.4%
ValueCountFrequency (%)
756804871
0.4%
418033181
0.4%
258207451
0.4%
193786461
0.4%
175151991
0.4%
119360641
0.4%
118331651
0.4%
112357451
0.4%
104749921
0.4%
101253481
0.4%

Interactions

2022-02-04T15:05:52.028537image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-04T15:05:31.112269image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-04T15:05:33.076818image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-04T15:05:34.682318image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-04T15:05:36.374057image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-04T15:05:38.188089image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-04T15:05:40.118929image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-04T15:05:41.725533image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-04T15:05:43.488940image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-04T15:05:45.016938image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-04T15:05:46.781336image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-04T15:05:48.409988image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-04T15:05:50.266286image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-04T15:05:52.129787image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-04T15:05:31.243443image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-04T15:05:33.201350image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-04T15:05:34.791361image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-04T15:05:36.511608image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-04T15:05:38.363629image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-04T15:05:40.281734image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-04T15:05:41.812244image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-04T15:05:43.583621image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-04T15:05:45.135580image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-04T15:05:46.879126image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-04T15:05:48.598803image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-04T15:05:50.398057image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-04T15:05:52.253367image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-04T15:05:31.432829image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-04T15:05:33.308038image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-04T15:05:34.878526image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-04T15:05:36.612279image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-04T15:05:38.478524image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-04T15:05:40.403400image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-04T15:05:41.898007image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-04T15:05:43.668338image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-04T15:05:45.247895image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-04T15:05:47.021735image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-04T15:05:48.705445image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-04T15:05:50.521101image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-04T15:05:52.505960image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-04T15:05:31.550504image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-04T15:05:33.415604image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-04T15:05:34.998129image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-04T15:05:36.760216image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-04T15:05:38.569291image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-04T15:05:40.542938image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-04T15:05:42.013769image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-04T15:05:43.807099image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-04T15:05:45.420434image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-04T15:05:47.171523image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-04T15:05:48.870715image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-04T15:05:50.660290image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-04T15:05:52.625560image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-04T15:05:31.715621image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-04T15:05:33.563774image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-04T15:05:35.128780image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-04T15:05:36.939850image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-04T15:05:38.665968image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-04T15:05:40.660021image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-04T15:05:42.151459image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-04T15:05:43.962583image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-04T15:05:45.559352image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-04T15:05:47.298492image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-04T15:05:49.154253image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-04T15:05:50.774925image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-04T15:05:52.756417image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-04T15:05:31.896593image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-04T15:05:33.741491image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-04T15:05:35.306322image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-04T15:05:37.119477image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-04T15:05:38.755652image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-04T15:05:40.804946image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-04T15:05:42.318019image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-04T15:05:44.148117image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-04T15:05:45.815393image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-04T15:05:47.437453image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-04T15:05:49.273609image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-04T15:05:50.928986image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-04T15:05:52.892967image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-04T15:05:32.057222image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-04T15:05:33.890092image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-04T15:05:35.425452image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-04T15:05:37.289790image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-04T15:05:38.906482image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-04T15:05:40.926683image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-04T15:05:42.558921image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-04T15:05:44.235819image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-04T15:05:45.941142image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-04T15:05:47.530141image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-04T15:05:49.413901image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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Correlations

2022-02-04T15:05:58.597258image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-02-04T15:05:58.792387image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-02-04T15:05:58.948656image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-02-04T15:05:59.196855image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-02-04T15:05:53.988332image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-02-04T15:05:54.200864image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-02-04T15:05:54.361657image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-02-04T15:05:54.489920image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

Unnamed: 0Province/StateCountry/RegionLatLong1/24/20221/25/20221/26/20221/27/20221/28/20221/29/20221/30/20221/31/20222/1/20222/2/2022
00NaNAfghanistan33.9391167.709953159896160252160692161004161057161290162111162926163555164190
11NaNAlbania41.1533020.168300248070248859251015252577254126254126255741258543258543261240
22NaNAlgeria28.033901.659600238885241406243568245698247568249310250774252117253520254885
33NaNAndorra42.506301.52180033025347013502835028355563555635556359583595836315
44NaNAngola-11.2027017.87390097263975949781297901980299805798076981169822698267
55NaNAntigua and Barbuda17.06080-61.7964006023602364426524655865586558662766276732
66NaNArgentina-38.41610-63.6167007940657804152081300238207752827163683136148335184837865684277788472848
77NaNArmenia40.0691045.038200352399353731355662358218361754364348366433367795370922374878
88Australian Capital TerritoryAustralia-35.47350149.01240031941330713393334418349763552036031364743702337426
99New South WalesAustralia-33.86880151.209300994277101549510333641083622110321911214811139787108648310982901110567

Last rows

Unnamed: 0Province/StateCountry/RegionLatLong1/24/20221/25/20221/26/20221/27/20221/28/20221/29/20221/30/20221/31/20222/1/20222/2/2022
270270NaNUnited Kingdom55.378100-3.43600015953685160477161614931916245474163339801640612316468522173158931742834517515199
271271NaNUruguay-32.522800-55.765800599040609785621453631019644631653853661441668425679878690496
272272NaNUzbekistan41.37749164.585262215063216186217360218477219663220815221919223008224023225110
273273NaNVanuatu-15.376700166.9592007777777777
274274NaNVenezuela6.423800-66.589700469566471389475135477022479011481375484021485974485974487775
275275NaNVietnam14.058324108.2771992155784217152721874812203208221813722332872263053227572722867502295494
276276NaNWest Bank and Gaza31.95220035.233200484979488599492694500444504992504992504992524716535160546176
277277NaNYemen15.55272748.51638810585105851082110888109421094210942110191106111113
278278NaNZambia-13.13389727.849332301924302569303266304002304353304656304922305047305557305959
279279NaNZimbabwe-19.01543829.154857228541228776228943229096229333229415229460229666229851230012